Update test weights and shapes
This commit is contained in:
D-X-Y
parent
d8784b3070
commit
b702ddf5a2
@ -4,18 +4,15 @@
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# Before run these commands, the files must be properly put.
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# python exps/NAS-Bench-201/test-weights.py --base_path $HOME/.torch/NAS-Bench-201-v1_0-e61699
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# python exps/NAS-Bench-201/test-weights.py --base_path $HOME/.torch/NAS-Bench-201-v1_1-096897 --dataset cifar10-valid --use_12 1 --use_valid 1
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# bash ./scripts-search/NAS-Bench-201/test-weights.sh cifar10-valid 1 1
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# bash ./scripts-search/NAS-Bench-201/test-weights.sh cifar10-valid 1
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###############################################################################################
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import os, gc, sys, time, glob, random, argparse
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import numpy as np
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import torch
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import torch.nn as nn
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from pathlib import Path
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from collections import OrderedDict
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from tqdm import tqdm
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lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
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from nas_201_api import NASBench201API as API
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from log_utils import time_string
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from models import get_cell_based_tiny_net
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@ -34,19 +31,22 @@ def tostr(accdict, norms):
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return ' '.join(xstr)
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def evaluate(api, weight_dir, data: str, use_12epochs_result: bool, valid_or_test: bool):
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def evaluate(api, weight_dir, data: str, use_12epochs_result: bool):
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print('\nEvaluate dataset={:}'.format(data))
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norms, accs = [], []
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final_accs = OrderedDict({'cifar10-valid': [], 'cifar10': [], 'cifar100': [], 'ImageNet16-120': []})
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norms = []
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final_val_accs = OrderedDict({'cifar10': [], 'cifar100': [], 'ImageNet16-120': []})
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final_test_accs = OrderedDict({'cifar10': [], 'cifar100': [], 'ImageNet16-120': []})
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for idx in range(len(api)):
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info = api.get_more_info(idx, data, use_12epochs_result=use_12epochs_result, is_random=False)
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if valid_or_test:
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accs.append(info['valid-accuracy'])
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else:
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accs.append(info['test-accuracy'])
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for key in final_accs.keys():
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for key in ['cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120']:
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info = api.get_more_info(idx, key, use_12epochs_result=False, is_random=False)
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final_accs[key].append(info['test-accuracy'])
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if key == 'cifar10-valid':
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final_val_accs['cifar10'].append(info['valid-accuracy'])
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elif key == 'cifar10':
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final_test_accs['cifar10'].append(info['test-accuracy'])
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else:
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final_test_accs[key].append(info['test-accuracy'])
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final_val_accs[key].append(info['valid-accuracy'])
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config = api.get_net_config(idx, data)
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net = get_cell_based_tiny_net(config)
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api.reload(weight_dir, idx)
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@ -60,14 +60,15 @@ def evaluate(api, weight_dir, data: str, use_12epochs_result: bool, valid_or_tes
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norms.append( float(np.mean(cur_norms)) )
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api.clear_params(idx, use_12epochs_result)
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if idx % 200 == 199 or idx + 1 == len(api):
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correlation = get_cor(norms, accs)
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head = '{:05d}/{:05d}'.format(idx, len(api))
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stem = tostr(final_accs, norms)
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print('{:} {:} {:} with {:} epochs on {:} : the correlation is {:.3f}. {:}'.format(time_string(), head, data, 12 if use_12epochs_result else 200, 'valid' if valid_or_test else 'test', correlation, stem))
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stem_val = tostr(final_val_accs, norms)
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stem_test = tostr(final_test_accs, norms)
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print('{:} {:} {:} with {:} epochs on {:} : the correlation is {:.3f}'.format(time_string(), head, data, 12 if use_12epochs_result else 200))
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print(' -->> {:} || {:}'.format(stem_val, stem_test))
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torch.cuda.empty_cache() ; gc.collect()
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def main(meta_file: str, weight_dir, save_dir, xdata, use_12epochs_result, valid_or_test):
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def main(meta_file: str, weight_dir, save_dir, xdata, use_12epochs_result):
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api = API(meta_file)
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datasets = ['cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120']
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print(time_string() + ' ' + '='*50)
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@ -83,7 +84,7 @@ def main(meta_file: str, weight_dir, save_dir, xdata, use_12epochs_result, valid
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print(time_string() + ' ' + '='*50)
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#evaluate(api, weight_dir, 'cifar10-valid', False, True)
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evaluate(api, weight_dir, xdata, use_12epochs_result, valid_or_test)
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evaluate(api, weight_dir, xdata, use_12epochs_result)
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print('{:} finish this test.'.format(time_string()))
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@ -94,7 +95,6 @@ if __name__ == '__main__':
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parser.add_argument('--base_path', type=str, default=None, help='The path to the NAS-Bench-201 benchmark file and weight dir.')
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parser.add_argument('--dataset' , type=str, default=None, help='.')
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parser.add_argument('--use_12' , type=int, default=None, help='.')
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parser.add_argument('--use_valid', type=int, default=None, help='.')
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args = parser.parse_args()
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save_dir = Path(args.save_dir)
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@ -104,5 +104,5 @@ if __name__ == '__main__':
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assert meta_file.exists(), 'invalid path for api : {:}'.format(meta_file)
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assert weight_dir.exists() and weight_dir.is_dir(), 'invalid path for weight dir : {:}'.format(weight_dir)
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main(str(meta_file), weight_dir, save_dir, args.dataset, bool(args.use_12), bool(args.use_valid))
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main(str(meta_file), weight_dir, save_dir, args.dataset, bool(args.use_12))
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@ -88,11 +88,7 @@ def evaluate_all_datasets(channels: Text, datasets: List[Text], xpaths: List[Tex
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def main(save_dir: Path, workers: int, datasets: List[Text], xpaths: List[Text],
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splits: List[int], seeds: List[int], nets: List[str], opt_config: Dict[Text, Any],
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srange: tuple, cover_mode: bool):
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assert torch.cuda.is_available(), 'CUDA is not available.'
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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.deterministic = True
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torch.set_num_threads(workers)
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to_evaluate_indexes: tuple, cover_mode: bool):
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log_dir = save_dir / 'logs'
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log_dir.mkdir(parents=True, exist_ok=True)
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@ -103,13 +99,13 @@ def main(save_dir: Path, workers: int, datasets: List[Text], xpaths: List[Text],
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logger.log('-' * 100)
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logger.log(
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'Start evaluating range =: {:06d} - {:06d} / {:06d} with cover-mode={:}'.format(srange[0], srange[1], len(nets),
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cover_mode))
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'Start evaluating range =: {:06d} - {:06d}'.format(min(to_evaluate_indexes), max(to_evaluate_indexes))
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+'({:} in total) / {:06d} with cover-mode={:}'.format(len(to_evaluate_indexes), len(nets), cover_mode))
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for i, (dataset, xpath, split) in enumerate(zip(datasets, xpaths, splits)):
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logger.log(
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'--->>> Evaluate {:}/{:} : dataset={:9s}, path={:}, split={:}'.format(i, len(datasets), dataset, xpath, split))
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logger.log('--->>> optimization config : {:}'.format(opt_config))
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to_evaluate_indexes = list(range(srange[0], srange[1] + 1))
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#to_evaluate_indexes = list(range(srange[0], srange[1] + 1))
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start_time, epoch_time = time.time(), AverageMeter()
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for i, index in enumerate(to_evaluate_indexes):
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@ -158,6 +154,40 @@ def traverse_net(candidates: List[int], N: int):
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return nets
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def filter_indexes(xlist, mode, save_dir, seeds):
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all_indexes = []
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for index in xlist:
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if mode == 'cover':
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all_indexes.append(index)
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else:
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for seed in seeds:
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temp_path = save_dir / 'arch-{:06d}-seed-{:04d}.pth'.format(index, seed)
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if not temp_path.exists():
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all_indexes.append(index)
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break
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print('{:} [FILTER-INDEXES] : there are {:} architectures in total'.format(time_string(), len(all_indexes)))
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SLURM_PROCID, SLURM_NTASKS = 'SLURM_PROCID', 'SLURM_NTASKS'
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if SLURM_PROCID in os.environ and SLURM_NTASKS in os.environ: # run on the slurm
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proc_id, ntasks = int(os.environ[SLURM_PROCID]), int(os.environ[SLURM_NTASKS])
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assert 0 <= proc_id < ntasks, 'invalid proc_id {:} vs ntasks {:}'.format(proc_id, ntasks)
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scales = [int(float(i)/ntasks*len(all_indexes)) for i in range(ntasks)] + [len(all_indexes)]
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per_job = []
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for i in range(ntasks):
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xs, xe = min(max(scales[i],0), len(all_indexes)-1), min(max(scales[i+1]-1,0), len(all_indexes)-1)
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per_job.append((xs, xe))
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for i, srange in enumerate(per_job):
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print(' -->> {:2d}/{:02d} : {:}'.format(i, ntasks, srange))
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current_range = per_job[proc_id]
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all_indexes = [all_indexes[i] for i in range(current_range[0], current_range[1]+1)]
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# set the device id
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device = proc_id % torch.cuda.device_count()
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torch.cuda.set_device(device)
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print(' set the device id = {:}'.format(device))
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print('{:} [FILTER-INDEXES] : after filtering there are {:} architectures in total'.format(time_string(), len(all_indexes)))
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return all_indexes
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='NAS-Bench-X', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('--mode', type=str, required=True, choices=['new', 'cover'], help='The script mode.')
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@ -182,15 +212,31 @@ if __name__ == '__main__':
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if not os.path.isfile(opt_config): raise ValueError('{:} is not a file.'.format(opt_config))
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save_dir = Path(args.save_dir) / 'raw-data-{:}'.format(args.hyper)
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save_dir.mkdir(parents=True, exist_ok=True)
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if not isinstance(args.srange, str) or len(args.srange.split('-')) != 2:
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if not isinstance(args.srange, str):
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raise ValueError('Invalid scheme for {:}'.format(args.srange))
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srange = args.srange.split('-')
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srangestr = "".join(args.srange.split())
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to_evaluate_indexes = set()
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for srange in srangestr.split(','):
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srange = srange.split('-')
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if len(srange) != 2: raise ValueError('invalid srange : {:}'.format(srange))
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assert len(srange[0]) == len(srange[1]) == 5, 'invalid srange : {:}'.format(srange)
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srange = (int(srange[0]), int(srange[1]))
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assert 0 <= srange[0] <= srange[1] < args.check_N, '{:} vs {:} vs {:}'.format(srange[0], srange[1], args.check_N)
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if not (0 <= srange[0] <= srange[1] < args.check_N):
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raise ValueError('{:} vs {:} vs {:}'.format(srange[0], srange[1], args.check_N))
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for i in range(srange[0], srange[1]+1):
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to_evaluate_indexes.add(i)
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assert len(args.seeds) > 0, 'invalid length of seeds args: {:}'.format(args.seeds)
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assert len(args.datasets) == len(args.xpaths) == len(args.splits), 'invalid infos : {:} vs {:} vs {:}'.format(len(args.datasets), len(args.xpaths), len(args.splits))
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if not (len(args.datasets) == len(args.xpaths) == len(args.splits)):
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raise ValueError('invalid infos : {:} vs {:} vs {:}'.format(len(args.datasets), len(args.xpaths), len(args.splits)))
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assert args.workers > 0, 'invalid number of workers : {:}'.format(args.workers)
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main(save_dir, args.workers, args.datasets, args.xpaths, args.splits, tuple(args.seeds), nets, opt_config,
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srange, args.mode == 'cover')
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target_indexes = filter_indexes(to_evaluate_indexes, args.mode, save_dir, args.seeds)
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assert torch.cuda.is_available(), 'CUDA is not available.'
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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.deterministic = True
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torch.set_num_threads(args.workers)
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main(save_dir, args.workers, args.datasets, args.xpaths, args.splits, tuple(args.seeds), nets, opt_config, target_indexes, args.mode == 'cover')
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@ -17,13 +17,13 @@ __all__ = ['evaluate_for_seed', 'pure_evaluate', 'get_nas_bench_loaders']
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def pure_evaluate(xloader, network, criterion=torch.nn.CrossEntropyLoss()):
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data_time, batch_time, batch = AverageMeter(), AverageMeter(), None
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losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
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latencies = []
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latencies, device = [], torch.cuda.current_device()
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network.eval()
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with torch.no_grad():
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end = time.time()
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for i, (inputs, targets) in enumerate(xloader):
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targets = targets.cuda(non_blocking=True)
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inputs = inputs.cuda(non_blocking=True)
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targets = targets.cuda(device=device, non_blocking=True)
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inputs = inputs.cuda(device=device, non_blocking=True)
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data_time.update(time.time() - end)
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# forward
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features, logits = network(inputs)
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@ -48,12 +48,12 @@ def procedure(xloader, network, criterion, scheduler, optimizer, mode: str):
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if mode == 'train' : network.train()
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elif mode == 'valid': network.eval()
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else: raise ValueError("The mode is not right : {:}".format(mode))
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device = torch.cuda.current_device()
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data_time, batch_time, end = AverageMeter(), AverageMeter(), time.time()
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for i, (inputs, targets) in enumerate(xloader):
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if mode == 'train': scheduler.update(None, 1.0 * i / len(xloader))
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targets = targets.cuda(non_blocking=True)
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targets = targets.cuda(device=device, non_blocking=True)
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if mode == 'train': optimizer.zero_grad()
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# forward
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features, logits = network(inputs)
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@ -84,7 +84,9 @@ def evaluate_for_seed(arch_config, opt_config, train_loader, valid_loaders, seed
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logger.log('FLOP = {:} MB, Param = {:} MB'.format(flop, param))
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# train and valid
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optimizer, scheduler, criterion = get_optim_scheduler(net.parameters(), opt_config)
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network, criterion = torch.nn.DataParallel(net).cuda(), criterion.cuda()
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default_device = torch.cuda.current_device()
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network = torch.nn.DataParallel(net, device_ids=[default_device]).cuda(device=default_device)
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criterion = criterion.cuda(device=default_device)
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# start training
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start_time, epoch_time, total_epoch = time.time(), AverageMeter(), opt_config.epochs + opt_config.warmup
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train_losses, train_acc1es, train_acc5es, valid_losses, valid_acc1es, valid_acc5es = {}, {}, {}, {}, {}, {}
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@ -1,12 +1,13 @@
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#!/bin/bash
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# bash ./scripts-search/NAS-Bench-201/test-weights.sh cifar10-valid 1 1
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# bash ./scripts-search/NAS-Bench-201/test-weights.sh cifar10-valid 1
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echo script name: $0
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echo $# arguments
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if [ "$#" -ne 3 ] ;then
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if [ "$#" -ne 2 ] ;then
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echo "Input illegal number of parameters " $#
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echo "Need 3 parameters for dataset, use_12_epoch, and use_validation_set"
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echo "Need 2 parameters for dataset and use_12_epoch"
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exit 1
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fi
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if [ "$TORCH_HOME" = "" ]; then
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echo "Must set TORCH_HOME envoriment variable for data dir saving"
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exit 1
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@ -17,4 +18,4 @@ fi
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OMP_NUM_THREADS=4 python exps/NAS-Bench-201/test-weights.py \
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--base_path $HOME/.torch/NAS-Bench-201-v1_1-096897 \
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--dataset $1 \
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--use_12 $2 --use_valid $3
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--use_12 $2
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@ -6,11 +6,13 @@
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# [mars6] bash ./scripts-search/X-X/train-shapes.sh 05001-10000 12 777
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# [mars20] bash ./scripts-search/X-X/train-shapes.sh 10001-14500 12 777
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# [mars20] bash ./scripts-search/X-X/train-shapes.sh 14501-19500 12 777
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# bash ./scripts-search/X-X/train-shapes.sh 19501-23500 12 777
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# bash ./scripts-search/X-X/train-shapes.sh 23501-27500 12 777
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# bash ./scripts-search/X-X/train-shapes.sh 27501-30000 12 777
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# bash ./scripts-search/X-X/train-shapes.sh 30001-32767 12 777
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# [saturn4] bash ./scripts-search/X-X/train-shapes.sh 19501-23500 12 777
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# [saturn4] bash ./scripts-search/X-X/train-shapes.sh 23501-27500 12 777
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# [saturn4] bash ./scripts-search/X-X/train-shapes.sh 27501-30000 12 777
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# [saturn4] bash ./scripts-search/X-X/train-shapes.sh 30001-32767 12 777
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#
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# CUDA_VISIBLE_DEVICES=2 bash ./scripts-search/X-X/train-shapes.sh 01000-03999,04050-05000,06000-09000,11000-14500,15000-18500,20000-23500,25000-27500,29000-30000 12 777
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# SLURM_PROCID=1 SLURM_NTASKS=5 bash ./scripts-search/X-X/train-shapes.sh 01000-03999,04050-05000,06000-09000,11000-14500,15000-18500,20000-23500,25000-27500,29000-30000 90 777
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echo script name: $0
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echo $# arguments
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if [ "$#" -ne 3 ] ;then
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